Cost-Based Assessment of Partitioning Algorithms of Agent-Based Systems on Hybrid Cloud Environments

TL;DR

This paper presents a cost-aware deployment model for agent-based simulators on hybrid clouds, evaluating partitioning algorithms to optimize both performance and monetary costs in distributed simulations.

Contribution

It introduces a novel cost-based assessment model that combines performance and cost criteria for deploying agent-based systems on hybrid cloud environments.

Findings

Effective partitioning improves simulation efficiency and cost savings.

Hybrid cloud environments can be optimized for specific agent-based models.

Cost-aware partitioning outperforms traditional methods in hybrid cloud deployments.

Abstract

Distributing agent-based simulators reveals many challenges while deploying them on a hybrid cloud infrastructure. In fact, a researcher's main motivations by running simulations on hybrid clouds, are reaching more scalable systems as well as reducing monetary costs. Indeed, hybrid cloud environment, despite providing scalability and effective control over proper data, requires an efficient deployment strategy combining both an efficient partitioning mechanism and cost savings. In this paper, we propose a cost deployment model dedicated to distributed agent-based simulation systems. This cost model, combining general performance partitioning criteria as well as monetary costs, is used to evaluate cluster and grid based partitioning algorithms on hybrid cloud environments. The first experimental results show that, for a given agent-based model, a good partitioning method used with the suitable hybrid cloud environment lead to an efficient and economic deployment.